

import argparse
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import (
    RobertaTokenizer,
    TrOCRConfig,
    TrOCRForCausalLM,
    TrOCRProcessor,
    VisionEncoderDecoderModel,
    ViTConfig,
    ViTImageProcessor,
    ViTModel,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowercase			:				Optional[Any]   =     logging.get_logger(__name__)






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_		):
							A      :					Dict      		=					[]
							for i in range(encoder_config.num_hidden_layers		):
														# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
														rename_keys.append(
														    (F'encoder.deit.blocks.{i}.norm1.weight', F'encoder.encoder.layer.{i}.layernorm_before.weight')		)
														rename_keys.append((F'encoder.deit.blocks.{i}.norm1.bias', F'encoder.encoder.layer.{i}.layernorm_before.bias')		)
														rename_keys.append(
														    (F'encoder.deit.blocks.{i}.attn.proj.weight', F'encoder.encoder.layer.{i}.attention.output.dense.weight')		)
														rename_keys.append(
														    (F'encoder.deit.blocks.{i}.attn.proj.bias', F'encoder.encoder.layer.{i}.attention.output.dense.bias')		)
														rename_keys.append(
														    (F'encoder.deit.blocks.{i}.norm2.weight', F'encoder.encoder.layer.{i}.layernorm_after.weight')		)
														rename_keys.append((F'encoder.deit.blocks.{i}.norm2.bias', F'encoder.encoder.layer.{i}.layernorm_after.bias')		)
														rename_keys.append(
														    (F'encoder.deit.blocks.{i}.mlp.fc1.weight', F'encoder.encoder.layer.{i}.intermediate.dense.weight')		)
														rename_keys.append(
														    (F'encoder.deit.blocks.{i}.mlp.fc1.bias', F'encoder.encoder.layer.{i}.intermediate.dense.bias')		)
														rename_keys.append(
														    (F'encoder.deit.blocks.{i}.mlp.fc2.weight', F'encoder.encoder.layer.{i}.output.dense.weight')		)
														rename_keys.append((F'encoder.deit.blocks.{i}.mlp.fc2.bias', F'encoder.encoder.layer.{i}.output.dense.bias')		)

							# cls token, position embeddings and patch embeddings of encoder
							rename_keys.extend(
							    [
							        ('''encoder.deit.cls_token''', '''encoder.embeddings.cls_token'''),
							        ('''encoder.deit.pos_embed''', '''encoder.embeddings.position_embeddings'''),
							        ('''encoder.deit.patch_embed.proj.weight''', '''encoder.embeddings.patch_embeddings.projection.weight'''),
							        ('''encoder.deit.patch_embed.proj.bias''', '''encoder.embeddings.patch_embeddings.projection.bias'''),
							        ('''encoder.deit.norm.weight''', '''encoder.layernorm.weight'''),
							        ('''encoder.deit.norm.bias''', '''encoder.layernorm.bias'''),
							    ]		)

							return rename_keys






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_		):
							for i in range(encoder_config.num_hidden_layers		):
														# queries, keys and values (only weights, no biases)
														A      :					Optional[int]      		=					state_dict.pop(F'encoder.deit.blocks.{i}.attn.qkv.weight'		)

														A      :					List[str]      		=					in_proj_weight[
														    : encoder_config.hidden_size, :
														]
														A      :					str      		=					in_proj_weight[
														    encoder_config.hidden_size : encoder_config.hidden_size * 2, :
														]
														A      :					Optional[Any]      		=					in_proj_weight[
														    -encoder_config.hidden_size :, :
														]






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		):
							A      :					int      		=					dct.pop(lowerCamelCase_		)
							A      :					List[Any]      		=					val






def        snake_case__		(    lowerCamelCase_		):
							if "handwritten" in checkpoint_url:
														A      :					Tuple      		=					'''https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg'''  # industry
														# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-12.jpg" # have
														# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-10.jpg" # let
														# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg"  #
														# url = "https://fki.tic.heia-fr.ch/static/img/a01-122.jpg"
							elif "printed" in checkpoint_url or "stage1" in checkpoint_url:
														A      :					Optional[int]      		=					'''https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg'''
							A      :					List[str]      		=					Image.open(requests.get(lowerCamelCase_     ,				stream=lowerCamelCase_		).raw		).convert('''RGB'''		)
							return im






@torch.no_grad()
def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_		):
							A      :					int      		=					ViTConfig(image_size=384     ,				qkv_bias=lowerCamelCase_		)
							A      :					Any      		=					TrOCRConfig()

							# size of the architecture
							if "base" in checkpoint_url:
														A      :					Tuple      		=					768
							elif "large" in checkpoint_url:
														# use ViT-large encoder
														A      :					str      		=					1024
														A      :					Dict      		=					4096
														A      :					Any      		=					24
														A      :					int      		=					16
														A      :					Optional[Any]      		=					1024
							else:
														raise ValueError('''Should either find \'base\' or \'large\' in checkpoint URL'''		)

							# the large-printed + stage1 checkpoints uses sinusoidal position embeddings, no layernorm afterwards
							if "large-printed" in checkpoint_url or "stage1" in checkpoint_url:
														A      :					Optional[Any]      		=					False
														A      :					Any      		=					'''relu'''
														A      :					Dict      		=					1024
														A      :					Optional[Any]      		=					True
														A      :					int      		=					False
														A      :					Union[str, Any]      		=					False

							# load HuggingFace model
							A      :					Tuple      		=					ViTModel(lowerCamelCase_     ,				add_pooling_layer=lowerCamelCase_		)
							A      :					str      		=					TrOCRForCausalLM(lowerCamelCase_		)
							A      :					int      		=					VisionEncoderDecoderModel(encoder=lowerCamelCase_     ,				decoder=lowerCamelCase_		)
							model.eval()

							# load state_dict of original model, rename some keys
							A      :					List[str]      		=					torch.hub.load_state_dict_from_url(lowerCamelCase_     ,				map_location='''cpu'''     ,				check_hash=lowerCamelCase_		)['''model''']

							A      :					Union[str, Any]      		=					create_rename_keys(lowerCamelCase_     ,				lowerCamelCase_		)
							for src, dest in rename_keys:
														rename_key(lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		)
							read_in_q_k_v(lowerCamelCase_     ,				lowerCamelCase_		)

							# remove parameters we don't need
							del state_dict["encoder.deit.head.weight"]
							del state_dict["encoder.deit.head.bias"]
							del state_dict["decoder.version"]

							# add prefix to decoder keys
							for key, val in state_dict.copy().items():
														A      :					int      		=					state_dict.pop(lowerCamelCase_		)
														if key.startswith('''decoder'''		) and "output_projection" not in key:
																					A      :					Dict      		=					val
														else:
																					A      :					Optional[Any]      		=					val

    # load state dict
							model.load_state_dict(lowerCamelCase_		)

							# Check outputs on an image
							A      :					List[Any]      		=					ViTImageProcessor(size=encoder_config.image_size		)
							A      :					Union[str, Any]      		=					RobertaTokenizer.from_pretrained('''roberta-large'''		)
							A      :					Union[str, Any]      		=					TrOCRProcessor(lowerCamelCase_     ,				lowerCamelCase_		)

							A      :					List[str]      		=					processor(images=prepare_img(lowerCamelCase_		)     ,				return_tensors='''pt'''		).pixel_values

							# verify logits
							A      :					List[str]      		=					torch.tensor([[model.config.decoder.decoder_start_token_id]]		)
							A      :					List[str]      		=					model(pixel_values=lowerCamelCase_     ,				decoder_input_ids=lowerCamelCase_		)
							A      :					Any      		=					outputs.logits

							A      :					Tuple      		=					torch.Size([1, 1, 50265]		)
							if "trocr-base-handwritten" in checkpoint_url:
														A      :					str      		=					torch.tensor(
														    [-1.4_502, -4.6_683, -0.5_347, -2.9_291, 9.1_435, -3.0_571, 8.9_764, 1.7_560, 8.7_358, -1.5_311]		)
							elif "trocr-large-handwritten" in checkpoint_url:
														A      :					Optional[int]      		=					torch.tensor(
														    [-2.6_437, -1.3_129, -2.2_596, -5.3_455, 6.3_539, 1.7_604, 5.4_991, 1.4_702, 5.6_113, 2.0_170]		)
							elif "trocr-base-printed" in checkpoint_url:
														A      :					List[Any]      		=					torch.tensor(
														    [-5.6_816, -5.8_388, 1.1_398, -6.9_034, 6.8_505, -2.4_393, 1.2_284, -1.0_232, -1.9_661, -3.9_210]		)
							elif "trocr-large-printed" in checkpoint_url:
														A      :					Tuple      		=					torch.tensor(
														    [-6.0_162, -7.0_959, 4.4_155, -5.1_063, 7.0_468, -3.1_631, 2.6_466, -0.3_081, -0.8_106, -1.7_535]		)

							if "stage1" not in checkpoint_url:
														assert logits.shape == expected_shape, "Shape of logits not as expected"
														assert torch.allclose(logits[0, 0, :10]     ,				lowerCamelCase_     ,				atol=1E-3		), "First elements of logits not as expected"

							Path(lowerCamelCase_		).mkdir(exist_ok=lowerCamelCase_		)
							print(F'Saving model to {pytorch_dump_folder_path}'		)
							model.save_pretrained(lowerCamelCase_		)
							print(F'Saving processor to {pytorch_dump_folder_path}'		)
							processor.save_pretrained(lowerCamelCase_		)


if __name__ == "__main__":
		lowercase			:				Optional[Any]   =     argparse.ArgumentParser()

		parser.add_argument(
		    "--checkpoint_url",
		    default="https://layoutlm.blob.core.windows.net/trocr/model_zoo/fairseq/trocr-base-handwritten.pt",
		    type=str,
		    help="URL to the original PyTorch checkpoint (.pth file).",
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
		)
		lowercase			:				Tuple   =     parser.parse_args()
		convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)









import math

import tensorflow as tf
from packaging import version






def        snake_case__		(    lowerCamelCase_		):
							A      :					Optional[int]      		=					tf.convert_to_tensor(lowerCamelCase_		)
							A      :					Union[str, Any]      		=					0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0		)     ,				x.dtype		)		))

							return x * cdf






def        snake_case__		(    lowerCamelCase_		):
							A      :					List[str]      		=					tf.convert_to_tensor(lowerCamelCase_		)
							A      :					Any      		=					tf.cast(math.pi     ,				x.dtype		)
							A      :					int      		=					tf.cast(0.044_715     ,				x.dtype		)
							A      :					Tuple      		=					0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi		) * (x + coeff * tf.pow(lowerCamelCase_     ,				3		))		))

							return x * cdf






def        snake_case__		(    lowerCamelCase_		):
							A      :					Union[str, Any]      		=					tf.convert_to_tensor(lowerCamelCase_		)

							return x * tf.tanh(tf.math.softplus(lowerCamelCase_		)		)






def        snake_case__		(    lowerCamelCase_		):
							A      :					Optional[int]      		=					tf.convert_to_tensor(lowerCamelCase_		)
							A      :					List[Any]      		=					tf.cast(0.044_715     ,				x.dtype		)
							A      :					str      		=					tf.cast(0.7_978_845_608     ,				x.dtype		)

							return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)		))






def        snake_case__		(    lowerCamelCase_		):
							A      :					List[str]      		=					tf.convert_to_tensor(lowerCamelCase_		)
							A      :					Any      		=					tf.cast(1.702     ,				x.dtype		)
							return x * tf.math.sigmoid(coeff * x		)






def        snake_case__		(    lowerCamelCase_		):
							return tf.clip_by_value(_gelu(lowerCamelCase_		)     ,				-10     ,				10		)






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_=-1		):
							A		,		A      :					Dict      		=					tf.split(lowerCamelCase_     ,				2     ,				axis=lowerCamelCase_		)
							return a * tf.math.sigmoid(lowerCamelCase_		)


if version.parse(tf.version.VERSION) >= version.parse("2.4"):






		def        snake_case__		(    lowerCamelCase_		):
									return tf.keras.activations.gelu(lowerCamelCase_     ,				approximate=lowerCamelCase_		)

		lowercase			:				Tuple   =     tf.keras.activations.gelu

		lowercase			:				Union[str, Any]   =     approximate_gelu_wrap
else:
		lowercase			:				str   =     _gelu
		lowercase			:				Dict   =     _gelu_new


lowercase			:				Union[str, Any]   =     {
    "gelu": gelu,
    "gelu_10": gelu_aa,
    "gelu_fast": gelu_fast,
    "gelu_new": gelu_new,
    "glu": glu,
    "mish": mish,
    "quick_gelu": quick_gelu,
    "relu": tf.keras.activations.relu,
    "sigmoid": tf.keras.activations.sigmoid,
    "silu": tf.keras.activations.swish,
    "swish": tf.keras.activations.swish,
    "tanh": tf.keras.activations.tanh,
}






def        snake_case__		(    lowerCamelCase_		):
							if activation_string in ACTaFN:
														return ACTaFN[activation_string]
							else:
														raise KeyError(F'function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()		)}'		)






